Timestamped data can provide important and useful information to organizations. Organizations can leverage timestamped data to obtain information needed to better serve their customers, to reduce waste, and to otherwise benefit the organization or other entities. Timestamped data can be modeled, forecast, mined, or otherwise processed to inform (interactive or automated) decision making.
In another example, manufacturers can leverage timestamped data relating to critical equipment to make decisions about maintenance scheduling to avoid critical component failures.
In another example, railroad companies can leverage timestamped data of shipments between various regions around the country to make decisions about where to stock rail cars to better meet predicted demand and minimize shipping delays.
In another example, energy companies can monitor and analyze timestamped data in real-time related to performance of wind turbines to quickly detect and respond to critical anomalous behavior and to maintain high turbine performance over time.
In another example, hospitals can aggregate timestamped medical patient data across various departments to better predict patient outcome and quickly detect and respond to potential healthcare issues.
As technologies continue to be developed that make capturing and collecting timestamped data easier than ever before, the sheer volume of timestamped data available to an organization can grow to be extremely large (e.g., hundreds of gigabytes to hundreds of terabytes and more). For example, the proliferation of internet of things (IOT) devices capable of user interaction and/or data sensing is generating a deluge of timestamped data that may be very useful to many organizations if it can be leveraged.
As the sizes of these databases of timestamped data increase, computational, architectural, and analytical challenges exist that can make it impractical or impossible for organizations to store and/or process these databases using conventional techniques. The database and computational expenses necessary to store and/or process the data can become infeasible for some organizations. In some cases, the sheer amount of memory necessary for processing such large amounts of data can quickly overwhelm an organization's hardware and communication resources. Further, timestamped data itself can be especially difficult to store and process in situations when the data must be sorted (by time) prior to analysis. Time series analysis requires time-ordered sequences of data. In some cases, simply moving timestamped data between various devices during analysis can become very computationally and communicatively expensive. As a result, it may be computationally infeasible for organizations to leverage all available timestamped data when making important decisions, which may result in less accurate predictions and missed opportunities in various fields. In the aforementioned examples, such missed opportunities could include not detecting an upcoming need for maintenance resulting in a critical hardware part failure in a predictive maintenance situation, not identifying an upcoming need for rail cars in a region resulting in undesired shipping delays in an industrial transportation situation, and not detecting a potential health issue for a hospital patient that would otherwise have been detected in a medical situation. Such missed opportunities may have been avoided had the organization been able to leverage more of this type of data in a computationally and communicatively efficient manner.